Downloads · 30 days
957
7% of all-time downloads
prithivMLmods/gemma-4-E4B-it-Uncensored-MAX-GGUF
gemma-4-E4B-it-Uncensored-MAX-GGUF is a image-text-to-text model from prithivMLmods. Use it for the image-text-to-text task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.
gemma-4-E4B-it-Uncensored-MAX is an uncensored evolution built on top of google/gemma-4-E4B-it. This model applies advanced refusal direction analysis and abliteration-based training strategies to significantly reduce…
Downloads · 30 days
957
7% of all-time downloads
All-time downloads
14.6K
Public
Repo size
71.7 GB
Likes
2
Public
Click a slice to open those files.
.gguf72.7 GB · 100%
From the Hugging Face model README
gemma-4-E4B-it-Uncensored-MAX is an uncensored evolution built on top of google/gemma-4-E4B-it. This model applies advanced refusal direction analysis and abliteration-based training strategies to significantly reduce internal refusal behaviors while preserving the reasoning and instruction-following strengths of the original architecture. The result is a powerful E4B parameter language model optimized for detailed responses and improved instruction adherence.
| File Name | Quant Type | File Size | File Link |
|---|---|---|---|
| gemma-4-E4B-it-Uncensored-MAX.BF16.gguf | BF16 | 15.1 GB | Download |
| gemma-4-E4B-it-Uncensored-MAX.F16.gguf | F16 | 15.1 GB | Download |
| gemma-4-E4B-it-Uncensored-MAX.F32.gguf | F32 | 30.1 GB | Download |
| gemma-4-E4B-it-Uncensored-MAX.Q8_0.gguf | Q8_0 | 8.01 GB | Download |
| gemma-4-E4B-it-Uncensored-MAX.mmproj-bf16.gguf | mmproj-bf16 | 992 MB | Download |
| gemma-4-E4B-it-Uncensored-MAX.mmproj-f16.gguf | mmproj-f16 | 992 MB | Download |
| gemma-4-E4B-it-Uncensored-MAX.mmproj-f32.gguf | mmproj-f32 | 1.91 GB | Download |
| gemma-4-E4B-it-Uncensored-MAX.mmproj-q8_0.gguf | mmproj-q8_0 | 560 MB | Download |
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):
